Transcript PPT - Blumberg lab home

BioSci 145B Lecture #10 6/8/2004
• Bruce Blumberg
– 2113E McGaugh Hall - office hours Wed 12-1 PM (or by appointment)
– phone 824-8573
– [email protected]
• TA – Curtis Daly [email protected]
– 2113 McGaugh Hall, 924-6873, 3116
– Office hours Tuesday 11-12
• lectures will be posted on web pages after lecture
– http://eee.uci.edu/04s/05705/ - link only here
– http://blumberg-serv.bio.uci.edu/bio145b-sp2004
– http://blumberg.bio.uci.edu/bio145b-sp2004
BioSci 145B lecture 10
page 1
©copyright
Bruce Blumberg 2004. All rights reserved
Library-based methods to map protein-protein interactions (contd)
• Phage display screening (a.k.a. panning)
– requires a library that expresses
inserts as fusion proteins with a
phage capsid protein
• most are M13 based
• some lambda phages used
– prepare target protein
• as affinity matrix
• or as radiolabeled probe
– test for interaction with library members
• if using affinity matrix you purify phages from a mixture
• if labeling protein one plates fusion protein library and probes with
the protein
– called receptor panning based on similarity with panning for
gold
BioSci 145B lecture 10
page 2
©copyright
Bruce Blumberg 2004. All rights reserved
Library-based methods to map protein-protein interactions (contd)
• Phage display screening (a.k.a. panning) (contd)
– advantages
• stringency can be manipulated
• if the affinity matrix approach works the cloning could go rapidly
– disadvantages
• Fusion proteins bias the screen against full-length cDNAs
• Multiple attempts required to optimize binding
• Limited targets possible
• may not work for heterodimers
• unlikely to work for complexes
• panning can take many months for each screen
– Greg Weiss in Chemistry is local expert
BioSci 145B lecture 10
page 3
©copyright
Bruce Blumberg 2004. All rights reserved
Mapping protein-protein interactions (contd)
• Two hybrid screening
– originally used in yeast, now
other systems possible
– prepare bait - target protein
fused to DBD (GAL4) usual
• stable cell line is commonly
used
– prepare fusion protein library
with an activation domain - prey
– Key factor required for success is
no activation domain in bait!
– approach
• transfect library into cells and either
select for survival or activation of
reporter gene
• purify and characterize positive clones
BioSci 145B lecture 10
page 4
©copyright
Bruce Blumberg 2004. All rights reserved
Mapping protein-protein interactions (contd)
• Two-hybrid screening (contd)
– Can be easily converted to
genome wide searching by
making haploid strains, each
containing one candidate
interactor
– Mate these and check for
growth or expression of reporter
gene
Bait plasmid
Prey plasmid
If interact, reporter expressed
and/or
Yeast survive
BioSci 145B lecture 10
page 5
©copyright
Bruce Blumberg 2004. All rights reserved
Molecular Interaction Screening - A New Approach to Protein Function
• Principle
– small pools of cDNAs are transcribed and translated in vitro to produce
pools of proteins that may be assayed in a variety of ways
• EMSA, co-ip, FRET, SPA
– cDNAs identified by protein function
• Starting material arrayed in 384-well plates
– Robotically pool source plates into daughter 96/384-well plates
• Pool size is optimizable - 96 works well
• Grow bacteria, prepare DNA,
TNT -> labeled protein
• Perform functional assay (SPA)
• Unpool positive wells into
components and rescreen
– Positive pools have known
composition
• only one second level
screen is required
BioSci 145B lecture 10
page 6
©copyright
Bruce Blumberg 2004. All rights reserved
Automated Molecular Interaction Screening
• Why do it this way?
– arbitrary size and complexity of target is possible
– Normalized cDNA pool -> representation of rare messages
– numerous possible endpoint assays
• radioactive, fluorescent, luminescent
– saturation screening of genome is feasible
– two screening steps to pure cDNA of interest in ~2 weeks
BioSci 145B lecture 10
page 7
©copyright
Bruce Blumberg 2004. All rights reserved
Large scale mapping of protein-protein interactions
• GST (glutathione-S-transferase)
pulldown assay
– Or other purification wherein one
protein is tagged and complex of
proteins binding to it is recovered
– Purify complexes from cells
– Characterize complexes by massspectrometry
– Iteratively build up a map of
protein interactions from such
complexes
BioSci 145B lecture 10
page 8
©copyright
Bruce Blumberg 2004. All rights reserved
Genomics - linking biological variation to disease pathophysiology
Biological system
Tissues
Populations
Cells
Animal strains
Patients
Clinical trial volunteers
Tissues
Stimulated / non-stimulated
Resistant / susceptible
Cases / controls
Responders / non-responders
Normal / treated-diseased
Multivariate!
Experimental system
protein
DNA
Variant between
individuals / populations
Genome sequence
Genotyping variation
RNA
Variant between tissues
Variant between tissues
Differential display
cDNA sequence (EST)
DNA microarrays
2D-electrophoresis / LC
Mass spectroscopy
( Yeast 2 hybrid )
What are genomic approaches to aid in these studies?
BioSci 145B lecture 10
page 9
©copyright
Bruce Blumberg 2004. All rights reserved
The rise of -omics
• The -omics revolution of science
– http://www.genomicglossaries.com/content/omes.asp
• What does it all mean?
– Transcriptomics – large scale gene profiling (usually microarray)
– Proteomics – study of complement of expressed proteins
– Functional genomics – very vague term, typically encompasses many
others
– Structural genomics – prediction of structure and interactions from
sequence
– Pharmacogenomics – transcriptional profiling of response to drug
treatment – often looking for genetic basis of differences
– Toxicogenomics – transcriptional profiling of response to toxicants (often
includes pharmacogenomics
• Seeks Mechanistic Understanding of Toxic Response
– Metabolomics – analysis of total metabolite pool ("metabolome") to
reveal novel aspects of cellular metabolism and global regulation
– Interactomics – genome wide study of macromolecular interactions,
physical and genetic are included.
BioSci 145B lecture 10
page 10
©copyright
Bruce Blumberg 2004. All rights reserved
The rise of –omics (contd)
• What do we want to know for drug development?
– How do individuals respond to drugs differently – pharmacogenomics
– How do individuals respond differently to toxicants - toxicogenomics
Target identification
Protein
Assay
Target validation
All of them!!
Compound library
Hit identification (HTS)
Hit
Genes
Hit to lead (Lead identification)
Lead optimisation
Candidate drug
Effort
Clinical trials
BioSci 145B lecture 10
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©copyright
Bruce Blumberg 2004. All rights reserved
Toxicogenomics
• Lump pharmacogenomics and toxicogenomics together in the context of drug
development
• Toxicology is the study of effects of toxicant exposure
– Traditional toxicology focuses on exposure, dose, effect
– “dose makes the poison” – overly simplistic and probably incorrect
• Mechanistic Toxicology (academic and regulatory)
– Investigative toxicology
• Hypothesis generation for grants and studies
– Risk assessment
• Understanding the mechanism of toxicity at the molecular level
• EPA and NIEHS very concerned with this
• Predictive toxicology
– Compound avoidance
• Elimination of liabilities (pharma)
– Compound selection
• Select compound with least toxic liability from a series (pharma)
– Compound management
• Tailor conventional studies and perform timely investigational
toxicology studies
BioSci 145B lecture 10
page 12
©copyright
Bruce Blumberg 2004. All rights reserved
Toxicogenomics (contd)
• Where predictive and mechanistic toxicology fit into drug development
– The road from hit to marketed drug is long
– 8/9 drug candidates fail due to toxic effects or unfavorable profile of
metabolism
Drug
Discovery
PreClinical
Testing
Clinical
Development
FDA
Mechanistic studies
Pattern-based
Mechanism-based
Predictive screens
BioSci 145B lecture 10
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©copyright
Bruce Blumberg 2004. All rights reserved
Phase
IV
Toxicogenomics (contd)
• Bioinformatics ties together toxicogenomic studies
• Overall goal is predictive, personalized medicine
– Provide personalized prescriptions to best help each patient
• Especially cancer therapy
Infrastructure
Clinical and experimental material
SNP Genotyping
Genome data
DNA
Novel targets
Novel pathways
Novel diagnostic indicators
Mining
Novel biomarkers
Predictive toxicology
Modelling Predictive pharmacology
Analysis
Microarray data
EST / cDNA data
RNA
protein
Proteomics
Predictive medicine
function
BioSci 145B lecture 10
Functional readouts
Metabolic space
Chemistry space
page 14
©copyright
Bruce Blumberg 2004. All rights reserved
Novelty, mechanism & prediction - toxicogenomics
Can we replace
animal studies with
genomics analyses?
Rat tissues
Normal and treated
Timecourses
BioSci 145B lecture 10
page 15
©copyright
Bruce Blumberg 2004. All rights reserved
Toxicogenomics (contd)
• What is toxicogenomics good for?
– Obtaining a high level view of a biological system
– Rapid generation of response profiles to
• Unravel mechanisms
• Discriminate among compounds
– Signature of exposures?
– Probably not a single method to identify toxicity
• Problems that must be solved
– Interlab variation – different labs use slightly different methods and get
results that may not be strictly applicable
• Japanese solution is to designate a single lab for entire country
– Most genes change expression at high doses of exposure
• Relevant?
BioSci 145B lecture 10
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©copyright
Bruce Blumberg 2004. All rights reserved
Genomic technology - implications
• Genetics and reverse genetics
– gene transfer and selection technology speeds up genetic analysis by
orders of magnitude
– virtually all conceivable experiments are now possible
• all questions are askable
• BUT should all questions be asked?
– much more straightforward to understand gene function using knockouts
and transgenics
• gene sequences are coming at an unprecedented rate from the
genome projects
• Knockouts and transgenics remain very expensive to practice
– other yet undiscovered technologies will be required to
understand gene function.
BioSci 145B lecture 10
page 17
©copyright
Bruce Blumberg 2004. All rights reserved
Genomic technology – implications (contd)
• Clinical genetics
– Molecular diagnostics are becoming very widespread as genes are
matched with diseases
• huge growth area for the future
• big pharma is dumping billions into diagnostics
– room for great benefit and widespread abuse
• diagnostics will enable early identification and treatment of diseases
• but insurance companies will want access to these data to maximize
profits
BioSci 145B lecture 10
page 18
©copyright
Bruce Blumberg 2004. All rights reserved
Genomic technology – implications (contd)
• gene therapy
– new viral vector technology is making this a reality
• efficient transfer and reasonable regulation possible
– long lag time from laboratory to clinic, still working with old technology
in many cases
– The Biotech Death of Jesse Gelsinger. Sheryl Gay Stolberg, NY Times,
Sunday Magazine, 28 Nov 99
• http://www.frenchanderson.org/history/biotech.pdf
• protein engineering
– not as widely appreciated as more glamorous techniques such as gene
therapy and transgenic crops
– better drugs, e.g., more stable insulin, TPA for heart attacks and
strokes, etc.
– more efficient enzymes (e.g. subtilisin in detergents)
– safe and effective vaccines
• just produce antigenic proteins rather than using inactivated or
attenuated organisms to reduce undesirable side effects
BioSci 145B lecture 10
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©copyright
Bruce Blumberg 2004. All rights reserved
Genomic technology – implications (contd)
• metabolite engineering
– enhanced microbial synthesis of valuable products
• eg indigo (jeans)
• vitamin C
– generation of entirely new small molecules
• transfer of antibiotic producing genes to related species yields new
antibiotics (badly needed)
– reduction of undesirable side reactions
• faster more efficient production of beer
• plants as producers of specialty chemicals
– underutilized because plant technology lags behind techniques in animals
• But regulations are strict (Monsanto)
– plants as factories to produce materials more cheaply and efficiently
• especially replacements for petrochemicals
– plants and herbs are the original source of many pharmaceutical products
• engineer them to overproduce desirable substances
BioSci 145B lecture 10
page 20
©copyright
Bruce Blumberg 2004. All rights reserved
Genomic technology – implications (contd)
• transgenic food
– gene transfer techniques have allowed the creation of desirable
mutations into animals and crops of commercial value
• disease resistance (various viruses)
• pest resistance (Bt cotton)
• Pesticide, herbicide and fungicide resistance
• growth hormone and milk production
– effective but necessary?
– negative implications – “Frankenfoods”
• pesticide and herbicide resistance lead to much higher use of toxic
compounds
• results are not predictable due to small datasets
• at least one herbicide (bromoxynil) for which resistance was
engineered has since been banned
• Atrazine is becoming highly controversial
BioSci 145B lecture 10
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©copyright
Bruce Blumberg 2004. All rights reserved